Off-the-shelf CNN features [1]} along with multi-scale pooling were first used for coral classification in [2]} on the Moorea Labelled Coral (MLC) dataset, which is a challenging dataset introduced in [3]}. This paper also explored a hybrid feature approach, combining CNN features with texton maps to further improve the classification accuracy on this dataset. Class imbalance is an additional problem which refers to the disproportionate difference in the amount of points allocated to some classes compared to others. This is a common issue in benthic marine datasets, as some species are significantly more abundant than others. To address the class imbalance, a cost-sensitive learning approach was studied in [4]} using off-the-shelf CNN features for MLC dataset. In another study, features extracted from pre-trained deep networks were used to generate coral population maps for the Abrolhos Islands in Western Australia [5]}. This study reported a trend of decreasing live coral cover in this region. This is consistent with the manual analysis of AUV images conducted by marine researchers [6]}, [7]}.
